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Massoud EC, Lee HK, Terando A, Wehner M. Bayesian weighting of climate models based on climate sensitivity. COMMUNICATIONS EARTH & ENVIRONMENT 2023; 4:365. [PMID: 38665200 PMCID: PMC11041668 DOI: 10.1038/s43247-023-01009-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 09/18/2023] [Indexed: 04/28/2024]
Abstract
Using climate model ensembles containing members that exhibit very high climate sensitivities to increasing CO2 concentrations can result in biased projections. Various methods have been proposed to ameliorate this 'hot model' problem, such as model emulators or model culling. Here, we utilize Bayesian Model Averaging as a framework to address this problem without resorting to outright rejection of models from the ensemble. Taking advantage of multiple lines of evidence used to construct the best estimate of the earth's climate sensitivity, the Bayesian Model Averaging framework produces an unbiased posterior probability distribution of model weights. The updated multi-model ensemble projects end-of-century global mean surface temperature increases of 2 oC for a low emissions scenario (SSP1-2.6) and 5 oC for a high emissions scenario (SSP5-8.5). These estimates are lower than those produced using a simple multi-model mean for the CMIP6 ensemble. The results are also similar to results from a model culling approach, but retain some weight on low-probability models, allowing for consideration of the possibility that the true value could lie at the extremes of the assessed distribution. Our results showcase Bayesian Model Averaging as a path forward to project future climate change that is commensurate with the available scientific evidence.
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Affiliation(s)
- Elias C. Massoud
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN USA
| | - Hugo K. Lee
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA USA
| | - Adam Terando
- U.S. Geological Survey, Southeast Climate Adaptation Science Center, Raleigh, NC USA
- Department of Applied Ecology, North Carolina State University, Raleigh, NC USA
| | - Michael Wehner
- Applied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA USA
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Regional and tele-connected impacts of the Tibetan Plateau surface darkening. Nat Commun 2023; 14:32. [PMID: 36596797 PMCID: PMC9810690 DOI: 10.1038/s41467-022-35672-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 12/16/2022] [Indexed: 01/04/2023] Open
Abstract
Despite knowledge of the presence of the Tibetan Plateau (TP) in reorganizing large-scale atmospheric circulation, it remains unclear how surface albedo darkening over TP will impact local glaciers and remote Asian monsoon systems. Here, we use a coupled land-atmosphere global climate model and a glacier model to address these questions. Under a high-emission scenario, TP surface albedo darkening will increase local temperature by 0.24 K by the end of this century. This warming will strengthen the elevated heat pump of TP, increasing South Asian monsoon precipitation while exacerbating the current "South Flood-North Drought" pattern over East Asia. The albedo darkening-induced climate change also leads to an accompanying TP glacier volume loss of 6.9%, which further increases to 25.2% at the equilibrium, with a notable loss in western TP. Our findings emphasize the importance of land-surface change responses in projecting future water resource availability, with important implications for water management policies.
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Thao S, Garvik M, Mariethoz G, Vrac M. Combining global climate models using graph cuts. CLIMATE DYNAMICS 2022; 59:2345-2361. [PMID: 36101674 PMCID: PMC9463255 DOI: 10.1007/s00382-022-06213-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 02/14/2022] [Indexed: 06/15/2023]
Abstract
UNLABELLED Global Climate Models are the main tools for climate projections. Since many models exist, it is common to use Multi-Model Ensembles to reduce biases and assess uncertainties in climate projections. Several approaches have been proposed to combine individual models and extract a robust signal from an ensemble. Among them, the Multi-Model Mean (MMM) is the most commonly used. Based on the assumption that the models are centered around the truth, it consists in averaging the ensemble, with the possibility of using equal weights for all models or to adjust weights to favor some models. In this paper, we propose a new alternative to reconstruct multi-decadal means of climate variables from a Multi-Model Ensemble, where the local performance of the models is taken into account. This is in contrast with MMM where a model has the same weight for all locations. Our approach is based on a computer vision method called graph cuts and consists in selecting for each grid point the most appropriate model, while at the same time considering the overall spatial consistency of the resulting field. The performance of the graph cuts approach is assessed based on two experiments: one where the ERA5 reanalyses are considered as the reference, and another involving a perfect model experiment where each model is in turn considered as the reference. We show that the graph cuts approach generally results in lower biases than other model combination approaches such as MMM, while at the same time preserving a similar level of spatial continuity. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s00382-022-06213-4.
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Affiliation(s)
- Soulivanh Thao
- Laboratoire des Sciences du Climat et l’Environnement (LSCE-IPSL) CNRS/CEA/UVSQ, UMR8212, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Mats Garvik
- Laboratoire des Sciences du Climat et l’Environnement (LSCE-IPSL) CNRS/CEA/UVSQ, UMR8212, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Gregoire Mariethoz
- Institute of Earth Surface Dynamics (IDYST), UNIL-Mouline, Geopolis, University of Lausanne, 1015 Lausanne, Switzerland
| | - Mathieu Vrac
- Laboratoire des Sciences du Climat et l’Environnement (LSCE-IPSL) CNRS/CEA/UVSQ, UMR8212, Université Paris-Saclay, Gif-sur-Yvette, France
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Sansom PG, Stephenson DB, Bracegirdle TJ. On Constraining Projections of Future Climate Using Observations and Simulations From Multiple Climate Models. J Am Stat Assoc 2021. [DOI: 10.1080/01621459.2020.1851696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Philip G. Sansom
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
| | - David B. Stephenson
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
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Murray NL, Holmes HA, Liu Y, Chang HH. A Bayesian ensemble approach to combine PM 2.5 estimates from statistical models using satellite imagery and numerical model simulation. ENVIRONMENTAL RESEARCH 2019; 178:108601. [PMID: 31465992 PMCID: PMC7048623 DOI: 10.1016/j.envres.2019.108601] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Revised: 07/09/2019] [Accepted: 07/21/2019] [Indexed: 05/21/2023]
Abstract
Ambient fine particulate matter less than 2.5 μm in aerodynamic diameter (PM2.5) has been linked to various adverse health outcomes. PM2.5 arises from both natural and anthropogenic sources, and PM2.5 concentrations can vary over space and time. However, the sparsity of existing air quality monitors greatly restricts the spatial-temporal coverage of PM2.5 measurements, potentially limiting the accuracy of PM2.5-related health studies. Various methods exist to address these limitations by supplementing air quality monitoring measurements with additional data. We develop a method to combine PM2.5 estimated from satellite-retrieved aerosol optical depth (AOD) and chemical transport model (CTM) simulations using statistical models. While most previous methods utilize AOD or CTM separately, we aim to leverage advantages offered by both data sources in terms of resolution and coverage using Bayesian ensemble averaging. Our approach differs from previous ensemble approaches in its ability to not only incorporate uncertainties in PM2.5 estimates from individual models but also to provide uncertainties for the resulting ensemble estimates. In an application of estimating daily PM2.5 in the Southeastern US, the ensemble approach outperforms previously developed spatial-temporal statistical models that use either AOD or bias-corrected CTM simulations in cross-validation (CV) analyses. More specifically, in spatially clustered CV experiments, the ensemble approach reduced the AOD-only and CTM-only model's root mean squared error (RMSE) by at least 13%. Similar improvements were seen in R2. The enhanced prediction performance that the ensemble technique provides at fine-scale spatial resolution, as well as the availability of prediction uncertainty, can be further used in health effect analyses of air pollution exposure.
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Affiliation(s)
- Nancy L Murray
- Emory University, Department of Biostatistics and Bioinformatics, Atlanta, GA, 30322, USA.
| | - Heather A Holmes
- University of Nevada, Reno, Department of Physics, Reno, NV, 89557, USA
| | - Yang Liu
- Emory University, Department of Environmental Health, Atlanta, GA, 30322, USA
| | - Howard H Chang
- Emory University, Department of Biostatistics and Bioinformatics, Atlanta, GA, 30322, USA
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Olson R, An SI, Fan Y, Chang W, Evans JP, Lee JY. A novel method to test non-exclusive hypotheses applied to Arctic ice projections from dependent models. Nat Commun 2019; 10:3016. [PMID: 31289260 PMCID: PMC6616623 DOI: 10.1038/s41467-019-10561-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Accepted: 05/20/2019] [Indexed: 11/09/2022] Open
Abstract
A major conundrum in climate science is how to account for dependence between climate models. This complicates interpretation of probabilistic projections derived from such models. Here we show that this problem can be addressed using a novel method to test multiple non-exclusive hypotheses, and to make predictions under such hypotheses. We apply the method to probabilistically estimate the level of global warming needed for a September ice-free Arctic, using an ensemble of historical and representative concentration pathway 8.5 emissions scenario climate model runs. We show that not accounting for model dependence can lead to biased projections. Incorporating more constraints on models may minimize the impact of neglecting model non-exclusivity. Most likely, September Arctic sea ice will effectively disappear at between approximately 2 and 2.5 K of global warming. Yet, limiting the warming to 1.5 K under the Paris agreement may not be sufficient to prevent the ice-free Arctic.
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Affiliation(s)
- R Olson
- Department of Atmospheric Sciences, Yonsei University, Seodaemun-gu, Seoul, 03722, South Korea
- Center for Climate Physics, Institute for Basic Science, Tonghapgigyegwan Building, Busandaehak-ro 63 beon-gil 2, Geumjeong-gu, Busan, 46241, South Korea
- Pusan National University, Geumjeong-gu, Busan, 46241, South Korea
| | - S-I An
- Department of Atmospheric Sciences, Yonsei University, Seodaemun-gu, Seoul, 03722, South Korea.
| | - Y Fan
- School of Mathematics and Statistics, UNSW Australia, Room 2055, Red Center, Sydney, 2052, Australia
| | - W Chang
- Division of Statistics and Data Science, Department of Mathematical Sciences, University of Cincinnati, 5516 French Hall, 2815 Commons Way, Cincinnati, OH, 45221-0025, USA
| | - J P Evans
- Climate Change Research Center and ARC Center for Excellence in Climate Extremes, UNSW Australia, 4th Level Mathews Building, Sydney, 2052, Australia
| | - J-Y Lee
- Center for Climate Physics, Institute for Basic Science, Tonghapgigyegwan Building, Busandaehak-ro 63 beon-gil 2, Geumjeong-gu, Busan, 46241, South Korea
- Research Center for Climate Sciences, Pusan National University, Room 1113, Tonghapgigyegwan Building, Busandaehak-ro 63 beon-gil 2, Geumjeong-gu, Busan, 46241, South Korea
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Olson R, An SI, Fan Y, Evans JP. Accounting for skill in trend, variability, and autocorrelation facilitates better multi-model projections: Application to the AMOC and temperature time series. PLoS One 2019; 14:e0214535. [PMID: 30969982 PMCID: PMC6457553 DOI: 10.1371/journal.pone.0214535] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Accepted: 03/14/2019] [Indexed: 11/24/2022] Open
Abstract
We present a novel quasi-Bayesian method to weight multiple dynamical models by their skill at capturing both potentially non-linear trends and first-order autocorrelated variability of the underlying process, and to make weighted probabilistic projections. We validate the method using a suite of one-at-a-time cross-validation experiments involving Atlantic meridional overturning circulation (AMOC), its temperature-based index, as well as Korean summer mean maximum temperature. In these experiments the method tends to exhibit superior skill over a trend-only Bayesian model averaging weighting method in terms of weight assignment and probabilistic forecasts. Specifically, mean credible interval width, and mean absolute error of the projections tend to improve. We apply the method to a problem of projecting summer mean maximum temperature change over Korea by the end of the 21st century using a multi-model ensemble. Compared to the trend-only method, the new method appreciably sharpens the probability distribution function (pdf) and increases future most likely, median, and mean warming in Korea. The method is flexible, with a potential to improve forecasts in geosciences and other fields.
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Affiliation(s)
- Roman Olson
- Department of Atmospheric Sciences, Yonsei University, Seoul, South Korea
- Center for Climate Physics, Institute for Basic Science, Busan, South Korea
- Pusan National University, Busan, South Korea
| | - Soon-Il An
- Department of Atmospheric Sciences, Yonsei University, Seoul, South Korea
| | - Yanan Fan
- School of Mathematics and Statistics, UNSW Australia, Sydney, New South Wales, Australia
| | - Jason P. Evans
- Climate Change Research Centre and ARC Centre for Excellence in Climate Extremes, UNSW Australia, Sydney, New South Wales, Australia
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9
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Climate Change Impacts on Nutrient Losses of Two Watersheds in the Great Lakes Region. WATER 2018. [DOI: 10.3390/w10040442] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Evaluation of Variations in Frequency of Landslide Events Affecting Pyroclastic Covers in Campania Region under the Effect of Climate Changes. HYDROLOGY 2017. [DOI: 10.3390/hydrology4030034] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Terando A, Keller K, Easterling WE. Probabilistic projections of agro-climate indices in North America. ACTA ACUST UNITED AC 2012. [DOI: 10.1029/2012jd017436] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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